Using electronic medical records to predict mortality in primary care patients with heart disease - Prognostic power and pathophysiologic implications

William M. Tierney, Blaine Y. Takesue, Dennis L. Vargo, Xiao Hua Zhou

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

OBJECTIVE: To identify high-risk patients with heart disease by using data stored in an electronic medical record system to predict six-year mortality. DESIGN: Retrospective cohort study. SETTING: Academic primary care general internal medicine practice affiliated with an urban teaching hospital with a state-of-the-art electronic medical record system. PATIENTS: Of 2,434 patients with evidence of ischemic heart disease or heart failure or both who visited an urban primary care practice in 1986, half were used to derive a proportional hazards model, and half were used to validate it. MEASUREMENTS: Mortality from any cause within six years of inception date. Model discrimination was assessed with the C statistic, and goodness-of-fit was measured with a calibration curve and Hosmer-Lemeshow statistic. MAIN RESULTS: Of these patients 82% had evidence of ischemic heart disease, 53% heart failure, and 35% both conditions. Mean survival among the 653 (27%) who died was 2.8 years; mean follow-up among survivors was 5.0 years. Those with both heart conditions had the highest mortality rate (45% at 6 years), followed by isolated heart failure (39%) and ischemic heart disease (18%). Of 300 potential predictive characteristics, 100 passed a univariate screen and were submitted to multivariable proportional hazards regression. Twelve variables contributed independent predictive information: age, weight, more than one previous hospitalization for heart failure, and nine conditions indicated on diagnostic tests and problem lists. No drug treatment variables were independent predictors. The model C statistic was 0.76 in the derivation sample of patients and 0.74 in a randomly selected validation sample, and it was well calibrated. Patients in the lowest and highest quartiles of risk differed more than five-fold in their average risk. CONCLUSIONS: Routine clinical data stored in patients' electronic medical records are capable of predicting mortality among patients with heart disease. This could allow increasingly scarce health care resources to be focused on those at highest mortality risk.

Original languageEnglish
Pages (from-to)83-91
Number of pages9
JournalJournal of General Internal Medicine
Volume11
Issue number2
DOIs
StatePublished - Feb 1996

Fingerprint

Electronic Health Records
Heart Diseases
Primary Health Care
Mortality
Heart Failure
Myocardial Ischemia
Health Resources
Urban Hospitals
Internal Medicine
Proportional Hazards Models
Routine Diagnostic Tests
Teaching Hospitals
Calibration
Survivors
Hospitalization
Cohort Studies
Retrospective Studies
Delivery of Health Care
Weights and Measures
Survival

Keywords

  • clinical epidemiology
  • clinical prediction
  • computerized record systems
  • congestive heart failure
  • coronary artery disease

ASJC Scopus subject areas

  • Internal Medicine

Cite this

Using electronic medical records to predict mortality in primary care patients with heart disease - Prognostic power and pathophysiologic implications. / Tierney, William M.; Takesue, Blaine Y.; Vargo, Dennis L.; Zhou, Xiao Hua.

In: Journal of General Internal Medicine, Vol. 11, No. 2, 02.1996, p. 83-91.

Research output: Contribution to journalArticle

@article{9f71c3bc0c5e4617ade58f32a7d1fd69,
title = "Using electronic medical records to predict mortality in primary care patients with heart disease - Prognostic power and pathophysiologic implications",
abstract = "OBJECTIVE: To identify high-risk patients with heart disease by using data stored in an electronic medical record system to predict six-year mortality. DESIGN: Retrospective cohort study. SETTING: Academic primary care general internal medicine practice affiliated with an urban teaching hospital with a state-of-the-art electronic medical record system. PATIENTS: Of 2,434 patients with evidence of ischemic heart disease or heart failure or both who visited an urban primary care practice in 1986, half were used to derive a proportional hazards model, and half were used to validate it. MEASUREMENTS: Mortality from any cause within six years of inception date. Model discrimination was assessed with the C statistic, and goodness-of-fit was measured with a calibration curve and Hosmer-Lemeshow statistic. MAIN RESULTS: Of these patients 82{\%} had evidence of ischemic heart disease, 53{\%} heart failure, and 35{\%} both conditions. Mean survival among the 653 (27{\%}) who died was 2.8 years; mean follow-up among survivors was 5.0 years. Those with both heart conditions had the highest mortality rate (45{\%} at 6 years), followed by isolated heart failure (39{\%}) and ischemic heart disease (18{\%}). Of 300 potential predictive characteristics, 100 passed a univariate screen and were submitted to multivariable proportional hazards regression. Twelve variables contributed independent predictive information: age, weight, more than one previous hospitalization for heart failure, and nine conditions indicated on diagnostic tests and problem lists. No drug treatment variables were independent predictors. The model C statistic was 0.76 in the derivation sample of patients and 0.74 in a randomly selected validation sample, and it was well calibrated. Patients in the lowest and highest quartiles of risk differed more than five-fold in their average risk. CONCLUSIONS: Routine clinical data stored in patients' electronic medical records are capable of predicting mortality among patients with heart disease. This could allow increasingly scarce health care resources to be focused on those at highest mortality risk.",
keywords = "clinical epidemiology, clinical prediction, computerized record systems, congestive heart failure, coronary artery disease",
author = "Tierney, {William M.} and Takesue, {Blaine Y.} and Vargo, {Dennis L.} and Zhou, {Xiao Hua}",
year = "1996",
month = "2",
doi = "10.1007/BF02599583",
language = "English",
volume = "11",
pages = "83--91",
journal = "Journal of General Internal Medicine",
issn = "0884-8734",
publisher = "Springer New York",
number = "2",

}

TY - JOUR

T1 - Using electronic medical records to predict mortality in primary care patients with heart disease - Prognostic power and pathophysiologic implications

AU - Tierney, William M.

AU - Takesue, Blaine Y.

AU - Vargo, Dennis L.

AU - Zhou, Xiao Hua

PY - 1996/2

Y1 - 1996/2

N2 - OBJECTIVE: To identify high-risk patients with heart disease by using data stored in an electronic medical record system to predict six-year mortality. DESIGN: Retrospective cohort study. SETTING: Academic primary care general internal medicine practice affiliated with an urban teaching hospital with a state-of-the-art electronic medical record system. PATIENTS: Of 2,434 patients with evidence of ischemic heart disease or heart failure or both who visited an urban primary care practice in 1986, half were used to derive a proportional hazards model, and half were used to validate it. MEASUREMENTS: Mortality from any cause within six years of inception date. Model discrimination was assessed with the C statistic, and goodness-of-fit was measured with a calibration curve and Hosmer-Lemeshow statistic. MAIN RESULTS: Of these patients 82% had evidence of ischemic heart disease, 53% heart failure, and 35% both conditions. Mean survival among the 653 (27%) who died was 2.8 years; mean follow-up among survivors was 5.0 years. Those with both heart conditions had the highest mortality rate (45% at 6 years), followed by isolated heart failure (39%) and ischemic heart disease (18%). Of 300 potential predictive characteristics, 100 passed a univariate screen and were submitted to multivariable proportional hazards regression. Twelve variables contributed independent predictive information: age, weight, more than one previous hospitalization for heart failure, and nine conditions indicated on diagnostic tests and problem lists. No drug treatment variables were independent predictors. The model C statistic was 0.76 in the derivation sample of patients and 0.74 in a randomly selected validation sample, and it was well calibrated. Patients in the lowest and highest quartiles of risk differed more than five-fold in their average risk. CONCLUSIONS: Routine clinical data stored in patients' electronic medical records are capable of predicting mortality among patients with heart disease. This could allow increasingly scarce health care resources to be focused on those at highest mortality risk.

AB - OBJECTIVE: To identify high-risk patients with heart disease by using data stored in an electronic medical record system to predict six-year mortality. DESIGN: Retrospective cohort study. SETTING: Academic primary care general internal medicine practice affiliated with an urban teaching hospital with a state-of-the-art electronic medical record system. PATIENTS: Of 2,434 patients with evidence of ischemic heart disease or heart failure or both who visited an urban primary care practice in 1986, half were used to derive a proportional hazards model, and half were used to validate it. MEASUREMENTS: Mortality from any cause within six years of inception date. Model discrimination was assessed with the C statistic, and goodness-of-fit was measured with a calibration curve and Hosmer-Lemeshow statistic. MAIN RESULTS: Of these patients 82% had evidence of ischemic heart disease, 53% heart failure, and 35% both conditions. Mean survival among the 653 (27%) who died was 2.8 years; mean follow-up among survivors was 5.0 years. Those with both heart conditions had the highest mortality rate (45% at 6 years), followed by isolated heart failure (39%) and ischemic heart disease (18%). Of 300 potential predictive characteristics, 100 passed a univariate screen and were submitted to multivariable proportional hazards regression. Twelve variables contributed independent predictive information: age, weight, more than one previous hospitalization for heart failure, and nine conditions indicated on diagnostic tests and problem lists. No drug treatment variables were independent predictors. The model C statistic was 0.76 in the derivation sample of patients and 0.74 in a randomly selected validation sample, and it was well calibrated. Patients in the lowest and highest quartiles of risk differed more than five-fold in their average risk. CONCLUSIONS: Routine clinical data stored in patients' electronic medical records are capable of predicting mortality among patients with heart disease. This could allow increasingly scarce health care resources to be focused on those at highest mortality risk.

KW - clinical epidemiology

KW - clinical prediction

KW - computerized record systems

KW - congestive heart failure

KW - coronary artery disease

UR - http://www.scopus.com/inward/record.url?scp=0029913065&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0029913065&partnerID=8YFLogxK

U2 - 10.1007/BF02599583

DO - 10.1007/BF02599583

M3 - Article

C2 - 8833015

AN - SCOPUS:0029913065

VL - 11

SP - 83

EP - 91

JO - Journal of General Internal Medicine

JF - Journal of General Internal Medicine

SN - 0884-8734

IS - 2

ER -